DEVOPS

Triage a failed preview smoke run and file a structured bug

When a preview smoke check fails, an agent inspects the browser trace and console errors, determines the likely root cause.

CategoryDevOps
Enginepaperclip
Difficultyadvanced
Triggerevent
Steps6
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerFailed preview smoke run received
  • ActionAgent reviews browser trace and console errors for root causeBrowserbase
  • ActionSearch Linear for a matching open issueLinearLinear
  • LogicDecide comment on existing vs create new
  • ActionFile Linear bug with repro steps and PR linkLinearLinear
  • OutputPost issue link back to the GitHub PRGitHubGitHub

What it does

When a preview smoke check fails this workflow doesn't just alert - an agent investigates. It reads the browser trace, console errors, and the failing step, reasons about the likely cause, checks for an existing matching issue, and files a clean Linear bug with repro steps linked back to the PR.

When to use it

Use it when smoke failures pile up faster than engineers can triage them, and you want each failure turned into an actionable, deduplicated ticket instead of a noisy Slack ping.

How it works

  1. 1A failed smoke run (from the gate workflow) triggers this flow with the trace, console log, and preview URL.
  2. 2The agent reviews the trace and console errors to identify the failing path and a probable root cause.
  3. 3It searches Linear for an open issue matching the same path and signature.
  4. 4A logic branch decides: comment on the existing issue, or create a new one.
  5. 5For a new bug, it files a Linear issue with reproduction steps, severity, and the PR link.
  6. 6It posts the issue link back to the PR thread so reviewers see the triage outcome.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect GitHubRepos, issues, pull requests, actions.
  2. 2
    Connect BrowserbaseHeadless browsers, sessions, replays.
  3. 3
    Connect LinearIssues, projects, cycles, triage.
  4. 4
    Connect OpenAIModels, embeddings, files.
  5. 5
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  6. 6
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  7. 7
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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